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1. Dual-hemisphere sea ice thickness reference measurements from multiple data sources for evaluation and product inter-comparison of satellite altimetry.

Author

Olsen, Ida Birgitte Lundtorp, Skourup, Henriette, Sallila, Heidi, Hendricks, Stefan, Hansen, Renée Mie Fredensborg, Kern, Stefan, Paul, Stephan, Bocquet, Marion, Fleury, Sara, Divine, Dmitry, and Rinne, Eero

Abstract

Sea ice altimetry currently remains the primary method for estimating sea ice thickness from space, however time-series of sea ice thickness estimates are of limited use without having been quality-controlled against reference measurements. Such reference observations for sea ice thickness validation in the polar regions are sparse and rarely presented in a format matching the satellite-derived products. Here, the first published comprehensive collection of sea ice reference observations including freeboard, thickness, draft and snow depth from sea ice-covered regions in the Northern Hemisphere (NH) and the Southern Hemisphere (SH) is presented. The observations have been collected using airborne sensors, autonomous drifting buoys, moored and submarine-mounted upward-looking sonars, and visual observations. The data package has been prepared to match the spatial (25 km for NH and 50 km for SH) and temporal (monthly) resolutions of conventional satellite altimetry-derived sea ice thickness data products for a direct evaluation of these. This data package, also known as the Climate Change Initiative (CCI) sea ice thickness (SIT) Round Robin Data Package (RRDP) was produced within the ESA CCI sea ice project. The current version of the CCI SIT RRDP covers the polar satellite altimetry era (1993–2021) and is part of ongoing efforts to keep the dataset updated. The CCI SIT RRDP has been collocated to satellite-derived sea ice thickness products from CryoSat-2, Envisat and ERS-1/2 produced within ESA CCI and the Fundamental Data Records for Altimetry (FDR4ALT) project to demonstrate the overlap and inter-comparison between the reference observations and satellite-derived products. Here, the CCI SIT RRDP is introduced along with examples of its use as a validation source for satellite altimetry products, where the averaging, collocation and uncertainty methodology is presented and their advantages and limitations are discussed. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Altimetry for the future: Building on 25 years of progress

Author

Abdalla, Saleh, Abdeh Kolahchi, Abdolnabi, Ablain, Michaël, Adusumilli, Susheel, Aich Bhowmick, Suchandra, Alou-Font, Eva, Amarouche, Laiba, Andersen, Ole Baltazar, Antich, Helena, Aouf, Lotfi, Arbic, Brian, Armitage, Thomas, Arnault, Sabine, Artana, Camila, Aulicino, Giuseppe, Ayoub, Nadia, Badulin, Sergei, Baker, Steven, Banks, Chris, Bao, Lifeng, Barbetta, Silvia, Barceló-Llull, Bàrbara, Barlier, François, Basu, Sujit, Bauer-Gottwein, Peter, Becker, Matthias, Beckley, Brian, Bellefond, Nicole, Belonenko, Tatyana, Benkiran, Mounir, Benkouider, Touati, Bennartz, Ralf, Benveniste, Jérôme, Bercher, Nicolas, Berge-Nguyen, Muriel, Bettencourt, Joao, Blarel, Fabien, Blazquez, Alejandro, Blumstein, Denis, Bonnefond, Pascal, Borde, Franck, Bouffard, Jérôme, Boy, François, Boy, Jean-Paul, Brachet, Cédric, Brasseur, Pierre, Braun, Alexander, Brocca, Luca, Brockley, David, Brodeau, Laurent, Brown, Shannon, Bruinsma, Sean, Bulczak, Anna, Buzzard, Sammie, Cahill, Madeleine, Calmant, Stéphane, Calzas, Michel, Camici, Stefania, Cancet, Mathilde, Capdeville, Hugues, Carabajal, Claudia Cristina, Carrere, Loren, Cazenave, Anny, Chassignet, Eric P., Chauhan, Prakash, Cherchali, Selma, Chereskin, Teresa, Cheymol, Cecile, Ciani, Daniele, Cipollini, Paolo, Cirillo, Francesca, Cosme, Emmanuel, Coss, Steve, Cotroneo, Yuri, Cotton, David, Couhert, Alexandre, Coutin-Faye, Sophie, Crétaux, Jean-François, Cyr, Frederic, d’Ovidio, Francesco, Darrozes, José, David, Cedric, Dayoub, Nadim, De Staerke, Danielle, Deng, Xiaoli, Desai, Shailen, Desjonqueres, Jean-Damien, Dettmering, Denise, Di Bella, Alessandro, Díaz-Barroso, Lara, Dibarboure, Gerald, Dieng, Habib Boubacar, Dinardo, Salvatore, Dobslaw, Henryk, Dodet, Guillaume, Doglioli, Andrea, Domeneghetti, Alessio, Donahue, David, Dong, Shenfu, Donlon, Craig, Dorandeu, Joël, Drezen, Christine, Drinkwater, Mark, Du Penhoat, Yves, Dushaw, Brian, Egido, Alejandro, Erofeeva, Svetlana, Escudier, Philippe, Esselborn, Saskia, Exertier, Pierre, Fablet, Ronan, Falco, Cédric, Farrell, Sinead Louise, Faugere, Yannice, Femenias, Pierre, Fenoglio, Luciana, Fernandes, Joana, Fernández, Juan Gabriel, Ferrage, Pascale, Ferrari, Ramiro, Fichen, Lionel, Filippucci, Paolo, Flampouris, Stylianos, Fleury, Sara, Fornari, Marco, Forsberg, Rene, Frappart, Frédéric, Frery, Marie-laure, Garcia, Pablo, Garcia-Mondejar, Albert, Gaudelli, Julia, Gaultier, Lucile, Getirana, Augusto, Gibert, Ferran, Gil, Artur, Gilbert, Lin, Gille, Sarah, Giulicchi, Luisella, Gómez-Enri, Jesús, Gómez-Navarro, Laura, Gommenginger, Christine, Gourdeau, Lionel, Griffin, David, Groh, Andreas, Guerin, Alexandre, Guerrero, Raul, Guinle, Thierry, Gupta, Praveen, Gutknecht, Benjamin D., Hamon, Mathieu, Han, Guoqi, Hauser, Danièle, Helm, Veit, Hendricks, Stefan, Hernandez, Fabrice, Hogg, Anna, Horwath, Martin, Idžanović, Martina, Janssen, Peter, Jeansou, Eric, Jia, Yongjun, Jia, Yuanyuan, Jiang, Liguang, Johannessen, Johnny A., Kamachi, Masafumi, Karimova, Svetlana, Kelly, Kathryn, Kim, Sung Yong, King, Robert, Kittel, Cecile M.M., Klein, Patrice, Klos, Anna, Knudsen, Per, Koenig, Rolf, Kostianoy, Andrey, Kouraev, Alexei, Kumar, Raj, Labroue, Sylvie, Lago, Loreley Selene, Lambin, Juliette, Lasson, Léa, Laurain, Olivier, Laxenaire, Rémi, Lázaro, Clara, Le Gac, Sophie, Le Sommer, Julien, Le Traon, Pierre-Yves, Lebedev, Sergey, Léger, Fabien, Legresy, Benoı̂t, Lemoine, Frank, Lenain, Luc, Leuliette, Eric, Levy, Marina, Lillibridge, John, Liu, Jianqiang, Llovel, William, Lyard, Florent, Macintosh, Claire, Makhoul Varona, Eduard, Manfredi, Cécile, Marin, Frédéric, Mason, Evan, Massari, Christian, Mavrocordatos, Constantin, Maximenko, Nikolai, McMillan, Malcolm, Medina, Thierry, Melet, Angelique, Meloni, Marco, Mertikas, Stelios, Metref, Sammy, Meyssignac, Benoit, Minster, Jean-François, Moreau, Thomas, Moreira, Daniel, Morel, Yves, Morrow, Rosemary, Moyard, John, Mulet, Sandrine, Naeije, Marc, Nerem, Robert Steven, Ngodock, Hans, Nielsen, Karina, Nilsen, Jan Even Øie, Niño, Fernando, Nogueira Loddo, Carolina, Noûs, Camille, Obligis, Estelle, Otosaka, Inès, Otten, Michiel, Oztunali Ozbahceci, Berguzar, P. Raj, Roshin, Paiva, Rodrigo, Paniagua, Guillermina, Paolo, Fernando, Paris, Adrien, Pascual, Ananda, Passaro, Marcello, Paul, Stephan, Pavelsky, Tamlin, Pearson, Christopher, Penduff, Thierry, Peng, Fukai, Perosanz, Felix, Picot, Nicolas, Piras, Fanny, Poggiali, Valerio, Poirier, Étienne, Ponce de León, Sonia, Prants, Sergey, Prigent, Catherine, Provost, Christine, Pujol, M-Isabelle, Qiu, Bo, Quilfen, Yves, Rami, Ali, Raney, R. Keith, Raynal, Matthias, Remy, Elisabeth, Rémy, Frédérique, Restano, Marco, Richardson, Annie, Richardson, Donald, Ricker, Robert, Ricko, Martina, Rinne, Eero, Rose, Stine Kildegaard, Rosmorduc, Vinca, Rudenko, Sergei, Ruiz, Simón, Ryan, Barbara J., Salaün, Corinne, Sanchez-Roman, Antonio, Sandberg Sørensen, Louise, Sandwell, David, Saraceno, Martin, Scagliola, Michele, Schaeffer, Philippe, Scharffenberg, Martin G., Scharroo, Remko, Schiller, Andreas, Schneider, Raphael, Schwatke, Christian, Scozzari, Andrea, Ser-giacomi, Enrico, Seyler, Frederique, Shah, Rashmi, Sharma, Rashmi, Shaw, Andrew, Shepherd, Andrew, Shriver, Jay, Shum, C.K., Simons, Wim, Simonsen, Sebatian B., Slater, Thomas, Smith, Walter, Soares, Saulo, Sokolovskiy, Mikhail, Soudarin, Laurent, Spatar, Ciprian, Speich, Sabrina, Srinivasan, Margaret, Srokosz, Meric, Stanev, Emil, Staneva, Joanna, Steunou, Nathalie, Stroeve, Julienne, Su, Bob, Sulistioadi, Yohanes Budi, Swain, Debadatta, Sylvestre-baron, Annick, Taburet, Nicolas, Tailleux, Rémi, Takayama, Katsumi, Tapley, Byron, Tarpanelli, Angelica, Tavernier, Gilles, Testut, Laurent, Thakur, Praveen K., Thibaut, Pierre, Thompson, LuAnne, Tintoré, Joaquín, Tison, Céline, Tourain, Cédric, Tournadre, Jean, Townsend, Bill, Tran, Ngan, Trilles, Sébastien, Tsamados, Michel, Tseng, Kuo-Hsin, Ubelmann, Clément, Uebbing, Bernd, Vergara, Oscar, Verron, Jacques, Vieira, Telmo, Vignudelli, Stefano, Vinogradova Shiffer, Nadya, Visser, Pieter, Vivier, Frederic, Volkov, Denis, von Schuckmann, Karina, Vuglinskii, Valerii, Vuilleumier, Pierrik, Walter, Blake, Wang, Jida, Wang, Chao, Watson, Christopher, Wilkin, John, Willis, Josh, Wilson, Hilary, Woodworth, Philip, Yang, Kehan, Yao, Fangfang, Zaharia, Raymond, Zakharova, Elena, Zaron, Edward D., Zhang, Yongsheng, Zhao, Zhongxiang, Zinchenko, Vadim, and Zlotnicki, Victor

Published
2021
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4. Arctic Freeboard and Snow Depth From Near-Coincident CryoSat-2 and ICESat-2 (CRYO2ICE) Observations:A First Examination of Winter Sea Ice During 2020–2022

Author

Fredensborg Hansen, Renée M., Skourup, Henriette, Rinne, Eero, Høyland, Knut V., Landy, Jack C., Merkouriadi, Ioanna, Forsberg, René, Fredensborg Hansen, Renée M., Skourup, Henriette, Rinne, Eero, Høyland, Knut V., Landy, Jack C., Merkouriadi, Ioanna, and Forsberg, René

Abstract

In the summer of 2020, ESA changed the orbit of CryoSat-2 to align periodically with NASA's ICESat-2 mission, a campaign known as CRYO2ICE, which allows for near-coincident CryoSat-2 and ICESat-2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat-2 and ICESat-2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along-track snow depth observations can provide high-resolution snow depth distributions which are vital for air-ice-ocean heat and momentum transfer, understanding light transmission, and snow-ice-interactions. Generally, ICESat-2 is backscattered at a surface above the elevation of the CryoSat-2 signal. CRYO2ICE snow depths are thinner than the daily model- or passive-microwave-based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite-derived and model-based snow estimates show similar seasonal accumulation over first-year ice, but CRYO2ICE has limited seasonal accumulation over multi-year ice which is linked to a slow increase in ICESat-2, and to some extent CryoSat-2, freeboards. We present a first estimation of spaceborne along-track snow depth estimates with average uncertainty of 10–11 ± 2–3 cm for 7-km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along-track dual-frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research.

Published
2024

5. Arctic Freeboard and Snow Depth From Near‐Coincident CryoSat‐2 and ICESat‐2 (CRYO2ICE) Observations: A First Examination of Winter Sea Ice During 2020–2022.

Author

Fredensborg Hansen, Renée M., Skourup, Henriette, Rinne, Eero, Høyland, Knut V., Landy, Jack C., Merkouriadi, Ioanna, and Forsberg, René

Subjects
SNOW accumulation, SEA ice, SPACE-based radar, OPTICAL radar, LASER altimeters, MOMENTUM transfer
Abstract

In the summer of 2020, ESA changed the orbit of CryoSat‐2 to align periodically with NASA's ICESat‐2 mission, a campaign known as CRYO2ICE, which allows for near‐coincident CryoSat‐2 and ICESat‐2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat‐2 and ICESat‐2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along‐track snow depth observations can provide high‐resolution snow depth distributions which are vital for air‐ice‐ocean heat and momentum transfer, understanding light transmission, and snow‐ice‐interactions. Generally, ICESat‐2 is backscattered at a surface above the elevation of the CryoSat‐2 signal. CRYO2ICE snow depths are thinner than the daily model‐ or passive‐microwave‐based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite‐derived and model‐based snow estimates show similar seasonal accumulation over first‐year ice, but CRYO2ICE has limited seasonal accumulation over multi‐year ice which is linked to a slow increase in ICESat‐2, and to some extent CryoSat‐2, freeboards. We present a first estimation of spaceborne along‐track snow depth estimates with average uncertainty of 10–11 ± 2–3 cm for 7‐km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along‐track dual‐frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research. Plain Language Summary: Estimates of snow depth on sea ice are currently either outdated or limited in resolution, thus a need to derive high‐resolution snow depth on sea ice is crucial. Former studies have computed snow depth on sea ice using the difference in penetration between radar (Ku‐band) and laser (or Ka‐band radar) altimeters from different satellite missions, assuming the Ku‐band radar and laser/Ka‐band are reflected at the bottom and top of the snow pack, respectively. Those studies have resulted in monthly composites of snow depth due to a limited overlap of individual tracks between the satellite missions. Since the CRYO2ICE (CryoSat‐2/ICESat‐2 Resonance) campaign was initiated in July 2020, we have for the first time the possibility of investigating near‐coincident observations of spaceborne radar and laser altimeters. These results are important to initiate relevant discussions on snow depth retrieval in preparation for the future dual‐frequency altimeter mission, CRISTAL (Copernicus Polar Ice and Snow Topography Altimeter), expected to launch in 2028. Key Points: CRYO2ICE (C2I) orbits are investigated over sea ice for the first time and snow depth is computed along the orbitsC2I snow depth has an uncertainty of 10–11 cm along 7 km segments with 5–95% confidence interval of 7–16 cm for both winter seasonsAlong‐track snow depth is compared with modeled and passive‐microwave estimates and C2I is thinner for the Pacific and Atlantic Arctic [ABSTRACT FROM AUTHOR]

Published
2024
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6. Satellite altimeter remote sensing of ice caps

Author

Rinne, Eero Juhani, Nienow, Peter., Shepherd, Andrew., and Newton, Anthony

Subjects
526.9, altimeter, ice cap, glacier
Abstract

This thesis investigates the use of satellite altimetry techniques for measuring surface elevation changes of ice caps. Two satellite altimeters, Radar Altimeter 2 (RA-2) and Geoscience Laser Altimeter System (GLAS) are used to assess the surface elevation changes of three Arctic ice caps. This is the first time the RA-2 has been used to assess the elevation changes of ice caps - targets much smaller than the ice sheets which are the instrument’s primary land ice targets. Algorithms for the retrieval of elevation change rates over ice caps using data acquired by RA-2 and GLAS are presented. These algorithms form a part of a European Space Agency (ESA) glacier monitoring system GlobGlacier. A comparison of GLAS elevation data to those acquired by the RA-2 shows agreement between the two instruments. Surface elevation change rate estimates based on RA-2 are given for three ice caps: Devon Ice Cap in Arctic Canada (−0.09 ± 0.29 m/a), Flade Isblink in Greenland (0.03 ± 0.03 m/a) and Austfonna on Svalbard (0.33 ± 0.08 m/a). Based on RA-2 and GLAS measurements it is shown that the areas of Flade Isblink below the late summer snow line have been thinning whereas the areas above the late summer snow line have been thickening. Also GLAS observed dynamic thickening rates of more than 3 m/a are presented. On Flade Isblink and Austfonna RA-2 measurements are compared to surface mass balance (SMB) estimates from a regional atmospheric climate model RACMO2. The comparison shows that SMB is the driver of interannual surface elevation changes at Austfonna. In contrast the comparison reveals areas on Flade Isblink where ice dynamics have an important effect on the surface elevation. Furthermore, RACMO2 estimates of surface mass budget at Austfonna before the satellite altimeter era are presented. This thesis shows that both traditional radar and laser satellite altimetry can be used to quantify the response of ice caps to the changing climate. Direct altimeter measurements of surface elevation and, in consequence volume change of ice caps, can be used to improve their mass budget estimates.

Published
2011

8. Arctic sea ice radar freeboard retrieval from the European Remote-Sensing Satellite (ERS-2) using altimetry: toward sea ice thickness observation from 1995 to 2021.

Author

Bocquet, Marion, Fleury, Sara, Piras, Fanny, Rinne, Eero, Sallila, Heidi, Garnier, Florent, and Rémy, Frédérique

Subjects
SEA ice, RADAR altimetry, RADAR, ALTIMETRY, OCEAN waves, TIME series analysis
Abstract

Sea ice volume's significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as the Environmental Satellite (Envisat) and especially the European Remote-Sensing Satellite (ERS-1 and ERS-2) have remained under-exploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic radar freeboard time series back to 1995. The difficulty in handling ERS measurements comes from a technical issue known as the pulse blurring effect, altering the radar echoes over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse blurring effect. To ensure consistency of the CryoSat-2, Envisat and ERS-2 time series, a multiparameter neural-network-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the mission-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good agreement between the missions, with a mean bias of 0.30 cm and a standard deviation of 9.7 cm for Envisat and CryoSat-2 and a 0.20 cm bias and a standard deviation of 3.8 cm for ERS-2 and Envisat. The monthly corrected radar freeboards obtained from Envisat and ERS-2 are then validated by comparison with several independent datasets such as airborne, mooring, direct-measurement and other altimeter products. Except for two datasets, comparisons lead to correlations ranging from 0.41 to 0.94 for Envisat and from 0.60 to 0.74 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from the ERS-2 mission to CryoSat-2). [ABSTRACT FROM AUTHOR]

Published
2023
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13. The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission

Author

Kern, Michael, Cullen, Robert, Berruti, Bruno, Bouffard, Jerome, Casal, Tania, Drinkwater, Mark R., Gabriele, Antonio, Lecuyot, Arnaud, Ludwig, Michael, Midthassel, Rolv, Navas Traver, Ignacio, Parrinello, Tommaso, Ressler, Gerhard, Andersson, Erik, Martin-Puig, Cristina, Andersen, Ole, Bartsch, Annett, Farrell, Sinead, Fleury, Sara, Gascoin, Simon, Guillot, Amandine, Humbert, Angelika, Rinne, Eero, Shepherd, Andrew, Van Den Broeke, Michiel R., Yackel, John, Sub Dynamics Meteorology, Marine and Atmospheric Research, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Sub Dynamics Meteorology, Marine and Atmospheric Research, Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, 02 engineering and technology, User requirements document, 01 natural sciences, law.invention, law, Component (UML), Altimeter, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, lcsh:Environmental sciences, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Copernicus, Water Science and Technology, Earth-Surface Processes, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, lcsh:GE1-350, Payload, Microwave radiometer, lcsh:QE1-996.5, Snow, lcsh:Geology, Radar altimeter, Environmental science
Abstract

The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) mission is one of six high-priority candidate missions (HPCMs) under consideration by the European Commission to enlarge the Copernicus Space Component. Together, the high-priority candidate missions fill gaps in the measurement capability of the existing Copernicus Space Component to address emerging and urgent user requirements in relation to monitoring anthropogenic CO2 emissions, polar environments, and land surfaces. The ambition is to enlarge the Copernicus Space Component with the high-priority candidate missions in the mid-2020s to provide enhanced continuity of services in synergy with the next generation of the existing Copernicus Sentinel missions. CRISTAL will carry a dual-frequency synthetic-aperture radar altimeter as its primary payload for measuring surface height and a passive microwave radiometer to support atmospheric corrections and surface-type classification. The altimeter will have interferometric capabilities at Ku-band for improved ground resolution and a second (non-interferometric) Ka-band frequency to provide information on snow layer properties. This paper outlines the user consultations that have supported expansion of the Copernicus Space Component to include the high-priority candidate missions, describes the primary and secondary objectives of the CRISTAL mission, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.

Published
2020

14. Reprocessing of the ERS-1, ERS-2 and ENVISAT missions: performances of the FDR4ALT products

Author

Piras, Fanny, Thibaut, Pierre, McMillan, Malcolm, Rinne, Eero, Fell, Frank, Nino, Fernando, Fleury, Sara, Tarpanelli, Angelica, Ollivier, Annabelle, Roinard, Hélène, Denneulin, Marie-Laure, Calmettes, Beatriz, Daguzé, Jean-Alexis, Woolliams, Emma, and Féménias, Pierre

Abstract

In the framework of the European Long Term Data Preservation Program (LTDP+), which aims at generating innovative Earth system data records named Fundamental Data Records (FDR) and Thematic Data Records (TDP), similar to level 2+ geophysical products, ESA/ESRIN has launched two years ago a reprocessing activity of the ERS-1, ERS-2 and ENVISAT altimeter and radiometer dataset. A large consortium of thematic experts has been formed to perform these activities which are 1) to define new tailored end-user products including the long, harmonized record of uncertainty-quantified observations, 2) to define the most appropriate and state-of-the-art level 1 and level 2 processing, 3) to reprocess the whole times series according to the upgraded ground processing and, 4) to validate the different products and provide them to a large community of users focused on the observation of the atmosphere, ocean topography, ocean waves, coastal, hydrology, sea ice, ice sheet regions. This activity will result in the production of 8 different datasets for each mission, each of them addressing a different need. The project kicked off in September 2019 and the first phase of definition and pre-validation is now completed. The production phase began in early 2022 and is expected to be finalized at the end of 2022. Results already show major improvements compared to the current ESA ERS and Envisat Altimetry products, on all surfaces. The objective of this talk is first to remind the OSTST members of this initiative, to explain the main guidelines, constraints, and status of the project and then to present the excellent performances of the FDR4ALT products. In particular, the presentation will show how the different communities (over global ocean, coastal, inland waters, sea ice, land ice, waves and atmosphere) will be able to benefit from this reprocessing to improve their long-term climate analysis.

Published
2022
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17. A means-corrected estimate for the Arctic sea-ice volume in 1990–2019

Author

Uotila, Petteri, Siponen, Joula, Rinne, Eero, Tietsche, Steffen, and INAR Physics

Subjects
1171 Geosciences, 114 Physical sciences
Abstract

Decadal changes in sea-ice thickness are one of the most visible signs of climate variability and change. To gain a comprehensive understanding of mechanisms involved, long time series, preferably with good uncertainty estimates, are needed. Importantly, the development of accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is compared to a set of five ocean reanalysis (ECCO-V4r4, GLORYS12V1, ORAS5 and PIOMAS). The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002–2017. The CCI product performs well in the validation of the reanalyses: overall root-mean-square difference (RMSD) between monthly sea-ice thickness from CCI and the reanalyses ranges from 0.4–1.2 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval. The CCI and reanalysis basin-scale sea-ice volumes have a good match in terms of year-to-year variability and long-term trends but rather different monthly mean climatologies. These findings provide a rationale to construct a multi-decadal sea-ice volume time series for the Arctic Ocean and its sub-basins from 1990–2019 by adjusting the ocean reanalyses ensemble toward CCI observations. Such a time series, including its uncertainty estimate, provides new insights to the evolution of the Arctic sea-ice volume during the past 30 years.

Published
2021

21. Classification of sea ice types in the arctic by radar echoes from saral/altika

Author

Hansen, Renée Mie Fredensborg, Rinne, Eero, Skourup, Henriette, Hansen, Renée Mie Fredensborg, Rinne, Eero, and Skourup, Henriette

Abstract

An important step in the sea ice freeboard to thickness conversion is the classification of sea ice types, since the ice type affects the snow depth and ice density. Studies using Ku-band CryoSat-2 have shown promise in distinguishing FYI and MYI based on the parametrisation of the radar echo. Here, we investigate applying the same classification algorithms that have shown success for Ku-band measurements to measurements acquired by SARAL/AltiKa at the Ka-band. Four different classifiers are investigated, i.e., the threshold-based, Bayesian, Random Forest (RF) and k-nearest neighbour (KNN), by using data from five 35 day cycles during Arctic mid-winter in 2014–2018. The overall classification performance shows the highest accuracy of 93% for FYI (Bayesian classifier) and 39% for MYI (threshold-based classifier). For all classification algorithms, more than half of the MYI cover falsely classifies as FYI, showing the difference in the surface characteristics attainable by Ka-band compared to Ku-band due to different scattering mechanisms. However, high overall classification performance (above 90%) is estimated for FYI for three supervised classifiers (KNN, RF and Bayesian). Furthermore, the leading-edge width parameter shows potential in discriminating open water (ocean) and sea ice when visually compared with reference data. Our results encourage the use of waveform parameters in the further validation of sea ice/open water edges and discrimination of sea ice types combining Ka- and Ku-band, especially with the planned launch of the dual-frequency altimeter mission Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) in 2027.

Published
2021

22. Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2

Author

Fredensborg Hansen, Renée Mie, Rinne, Eero, Farrell, Sinéad Louise, Skourup, Henriette, Fredensborg Hansen, Renée Mie, Rinne, Eero, Farrell, Sinéad Louise, and Skourup, Henriette

Abstract

We present a comparison of Ice, Cloud and land Elevation Satellite-2 (ICESat-2) geolocated photon heights and operational ice charts from the Finnish Ice Service in the Bay of Bothnia in spring 2019. We show that ICESat-2 (IS2) retrievals from ice areas with different ridging characteristics, more precisely the degree of ice ridging (DIR), differ significantly. DIR is a particularly useful parameter for ice navigators, as it provides information on how difficult it is to navigate through an area based on e.g. sail heights and distribution of sea ice ridges. DIR estimates are included in ice charts of the Baltic Sea and are based primarily on in situ observations from an active icebreaker fleet. We show that DIR may potentially be estimated from IS2. We also present a comparison of IS2 measurements and Sentinel-1 synthetic aperture radar frames, discussing several individual cases of IS2 photon elevation behaviour over Baltic sea ice. We suggest that IS2 data can be of benefit to international ice services, especially if a time-critical photon height product were to be made available. Furthermore, we show that the difference between highest and mean photon elevations (elevation anomalies) of IS2 correspond to expected ridge sail heights in our study area. Our study is one of the first steps in creating sea ice applications beyond the traditional goal of freeboard and thickness retrieval for IS2.

Published
2021

23. Arctic sea ice radar freeboard retrieval from ERS-2 using altimetry: Toward sea ice thickness observation from 1995 to 2021.

Author

Bocquet, Marion, Fleury, Sara, Piras, Fanny, Rinne, Eero, Sallila, Heidi, Garnier, Florent, and Rémy, Frédérique

Abstract

Sea ice volume significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as Envisat and especially ERS-1 and ERS-2 have remained under exploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic freeboard time series back to 1995. The difficulty to handle ERS measurements comes from a technical issue known as the pulse-blurring effect, altering the radar echos over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse-blurring effect. To ensure consistency of the CryoSat-2/Envisat/ERS-2 time series, a multi-parameters neural network-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the missions-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good consistencies between missions, with a mean bias of 3 mm for Envisat/CryoSat-2 and 2 mm for ERS-2/Envisat. The monthly maps obtained from Envisat and ERS-2 are then validated by comparison with several independent data such as airborne, moorings, direct measurements and other altimeter products. Except for two data sets, comparisons lead to correlation ranging from 0.42 to 0.94 for Envisat, and 0.6 to 0.76 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from ERS-2 mission to CryoSat-2). [ABSTRACT FROM AUTHOR]

Published
2022
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25. Assessment of NASA’s ICESat-2 photon counting laser altimeter in the Baltic Sea to support ice mapping estimates of ridging and local bathymetry

Author

Rinne, Eero, Skourup, Henriette, Insinööritieteiden korkeakoulu, Praks, Jaan, Fredensborg Hansen, Renée, Rinne, Eero, Skourup, Henriette, Insinööritieteiden korkeakoulu, Praks, Jaan, and Fredensborg Hansen, Renée

Abstract

The Ice, Cloud and land Elevation Satellite (ICESat-2) is the first Earth-orbiting photon counting laser altimeter. The novel measurements strategy allows for unprecedented global elevation measurements useful for measuring sea ice and shallow water bathymetry at high resolution. This thesis will assess how well ICESat-2 photon observations compare with ice charts from the Finnish Ice Service (FIS) and estimate ridging features in the Baltic, and asses to what extent shallow water bathymetry in the Baltic can be derived. With sea ice being a main concern of a navigator in ice-covered regions, it is necessary to provide a deformation numeral of degree of ice ridging (DIR) to aid navigation, which provides guidance on whether a ship can safely pass through an area. The FIS ice charts provide DIR information based on ice breaker observations and SAR images. With the high-resolution and dense surface elevation observations from ICESat-2, this thesis has shown it possible to differentiate and estimate DIR. Aggregating segments of 150 photons and estimating relative elevation measurements on ridging proved able separate the observations into different levels of deformation. Future work includes additional filtration schemes to remove erroneous photons from low-laying clouds, fog and/or background sun events. These results are the first to showcase ICESat-2’s ability to measure small-scale roughness structures and encourages scientists to explore ICESat-2 photons for sea ice pressure ridge detection. Shallow water bathymetry is notoriously difficult to map, but of great interest to science applications, ship navigation and engineering applications. Presented here are the first results of shallow water bathymetry in the murky and eutrophicated waters of the Baltic. Maximum depth down to 16~m depth and average maximum depth of 8.58~m was identified and based on monthly maximum Secchi depths (1979-2018), ICESat-2 proved able to retrieve depths up to 0.87 of the maximum Secchi

Published
2020

26. Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

Author

Mäkynen, Marko, Haapala, Jari, Aulicino, Giuseppe, Balan-sarojini, Beena, Balmaseda, Magdalena, Gegiuc, Alexandru, Ardhuin, Fanny, Hendricks, Stefan, Heygster, Georg, Istomina, Larysa, Kaleschke, Lars, Karvonen, Juha, Krumpen, Thomas, Lensu, Mikko, Mayer, Michael, Parmiggiani, Flavio, Ricker, Robert, Rinne, Eero, Schmitt, Amélie, Similä, Markku, Tietsche, Steffen, Tonboe, Rasmus, Wadhams, Peter, Winstrup, Mai, Zuo, Hao, Mäkynen, Marko, Haapala, Jari, Aulicino, Giuseppe, Balan-sarojini, Beena, Balmaseda, Magdalena, Gegiuc, Alexandru, Ardhuin, Fanny, Hendricks, Stefan, Heygster, Georg, Istomina, Larysa, Kaleschke, Lars, Karvonen, Juha, Krumpen, Thomas, Lensu, Mikko, Mayer, Michael, Parmiggiani, Flavio, Ricker, Robert, Rinne, Eero, Schmitt, Amélie, Similä, Markku, Tietsche, Steffen, Tonboe, Rasmus, Wadhams, Peter, Winstrup, Mai, and Zuo, Hao

Abstract

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.

Published
2020
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27. The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission

Author

Sub Dynamics Meteorology, Marine and Atmospheric Research, Kern, Michael, Cullen, Robert, Berruti, Bruno, Bouffard, Jerome, Casal, Tania, Drinkwater, Mark R., Gabriele, Antonio, Lecuyot, Arnaud, Ludwig, Michael, Midthassel, Rolv, Navas Traver, Ignacio, Parrinello, Tommaso, Ressler, Gerhard, Andersson, Erik, Martin-Puig, Cristina, Andersen, Ole, Bartsch, Annett, Farrell, Sinead, Fleury, Sara, Gascoin, Simon, Guillot, Amandine, Humbert, Angelika, Rinne, Eero, Shepherd, Andrew, Van Den Broeke, Michiel R., Yackel, John, Sub Dynamics Meteorology, Marine and Atmospheric Research, Kern, Michael, Cullen, Robert, Berruti, Bruno, Bouffard, Jerome, Casal, Tania, Drinkwater, Mark R., Gabriele, Antonio, Lecuyot, Arnaud, Ludwig, Michael, Midthassel, Rolv, Navas Traver, Ignacio, Parrinello, Tommaso, Ressler, Gerhard, Andersson, Erik, Martin-Puig, Cristina, Andersen, Ole, Bartsch, Annett, Farrell, Sinead, Fleury, Sara, Gascoin, Simon, Guillot, Amandine, Humbert, Angelika, Rinne, Eero, Shepherd, Andrew, Van Den Broeke, Michiel R., and Yackel, John

Published
2020

28. Surface Properties Linked to Retrieval Uncertainty of Satellite Sea-Ice Thickness with Upward-Looking Sonar Measurements.

Author

Khvorostovsky, Kirill, Hendricks, Stefan, Rinne, Eero, Khvorostovsky, Kirill, Hendricks, Stefan, and Rinne, Eero

Abstract

One of the key sources of uncertainties in sea ice freeboard and thickness estimates derived from satellite radar altimetry results from changes in sea ice surface properties. In this study, we analyse this effect, comparing upward-looking sonar (ULS) measurements in the Beaufort Sea over the period 2003–2018 to sea ice draft derived from Envisat and Cryosat-2 data. We show that the sea ice draft growth underestimation observed for the most of winter seasons depends on the surface properties preconditioned by the melt intensity during the preceding summer. The comparison of sea ice draft time series in the Cryosat-2 era indicates that applying 50% retracker thresholds, used to produce the European Space Agency’s Climate Change Initiative (CCI) product, provide better agreement between satellite retrievals and ULS data than the 80% threshold that is closer to the expected physical waveform interpretation. Our results, therefore, indicate compensating error contributions in the full end-to-end sea-ice thickness processing chain, which prevents the quantification of individual factors with sea-ice thickness/draft validation data alone

Published
2020

29. Satellite observations for detecting and forecasting sea-ice conditions:A summary of advances made in the SPICES Project by the EU's Horizon 2020 Programme

Author

Mäkynen, Marko, Haapala, Jari, Aulicino, Giuseppe, Balan-Sarojini, Beena, Balmaseda, Magdalena, Gegiuc, Alexandru, Girard-Ardhuin, Fanny, Hendricks, Stefan, Heygster, Georg, Istomina, Larysa, Kaleschke, Lars, Karvonen, Juha, Krumpen, Thomas, Lensu, Mikko, Mayer, Michael, Parmiggiani, Flavio, Ricker, Robert, Rinne, Eero, Schmitt, Amelie, Similä, Markku, Tietsche, Steffen, Tonboe, Rasmus, Wadhams, Peter, Winstrup, Mai, Zuo, Hao, Mäkynen, Marko, Haapala, Jari, Aulicino, Giuseppe, Balan-Sarojini, Beena, Balmaseda, Magdalena, Gegiuc, Alexandru, Girard-Ardhuin, Fanny, Hendricks, Stefan, Heygster, Georg, Istomina, Larysa, Kaleschke, Lars, Karvonen, Juha, Krumpen, Thomas, Lensu, Mikko, Mayer, Michael, Parmiggiani, Flavio, Ricker, Robert, Rinne, Eero, Schmitt, Amelie, Similä, Markku, Tietsche, Steffen, Tonboe, Rasmus, Wadhams, Peter, Winstrup, Mai, and Zuo, Hao

Abstract

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015-2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU's Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.

Published
2020

34. Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

Author

Mäkynen, Marko, primary, Haapala, Jari, additional, Aulicino, Giuseppe, additional, Balan-Sarojini, Beena, additional, Balmaseda, Magdalena, additional, Gegiuc, Alexandru, additional, Girard-Ardhuin, Fanny, additional, Hendricks, Stefan, additional, Heygster, Georg, additional, Istomina, Larysa, additional, Kaleschke, Lars, additional, Karvonen, Juha, additional, Krumpen, Thomas, additional, Lensu, Mikko, additional, Mayer, Michael, additional, Parmiggiani, Flavio, additional, Ricker, Robert, additional, Rinne, Eero, additional, Schmitt, Amelie, additional, Similä, Markku, additional, Tietsche, Steffen, additional, Tonboe, Rasmus, additional, Wadhams, Peter, additional, Winstrup, Mai, additional, and Zuo, Hao, additional

Published
2020
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35. Using the Baltic Sea to advance algorithms to extract altimetry-derived sea-level data from complex coastal areas, featuring seasonal sea-ice

Author

Passaro, Marcello, primary, Müller, Felix L., additional, Abulaitijiang, Adili, additional, Andersen, Ole B., additional, Dettmering, Denise, additional, Høyer, Jacob L., additional, Johansson, Milla, additional, Oelsmann, Julius, additional, Rautiainen, Laura, additional, Ringgaard, Ida M., additional, Rinne, Eero, additional, Särkkä, Jani, additional, Scarrott, Rory, additional, Schwatke, Christian, additional, Seitz, Florian, additional, Skovgaard Madsen, Kristine, additional, Tuomi, Laura, additional, Ambrozio, Americo, additional, Restano, Marco, additional, and Benveniste, Jérôme, additional

Published
2020
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37. The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL): Expected Mission Contributions

Author

Kern, Michael, primary, Cullen, Robert, additional, Berruti, Bruno, additional, Bouffard, Jerome, additional, Casal, Tania, additional, Drinkwater, Mark R., additional, Gabriele, Antonio, additional, Lecuyot, Arnaud, additional, Ludwig, Michael, additional, Midthassel, Rolv, additional, Navas Traver, Ignacio, additional, Parrinello, Tommaso, additional, Ressler, Gerhard, additional, Andersson, Eric, additional, Martin Puig, Cristina, additional, Andersen, Ole, additional, Bartsch, Annett, additional, Farrell, Sinead L., additional, Fleury, Sara, additional, Gascoin, Simon, additional, Guillot, Amandine, additional, Humbert, Angelika, additional, Rinne, Eero, additional, Shepherd, Andrew, additional, van den Broeke, Michiel R., additional, and Yackel, John, additional

Published
2020
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40. Retrieving Sea Level and Freeboard in the Arctic: A Review of Current Radar Altimetry Methodologies and Future Perspectives

Author

Quartly, Graham D., Rinne, Eero, Passaro, Marcello, Andersen, Ole B., Dinardo, Salvatore, Fleury, Sara, Guillot, Amandine, Hendricks, Stefan, Kurekin, Andrey A., Müller, Felix L., Ricker, Robert, Skourup, Henriette, Tsamados, Michel, Quartly, Graham D., Rinne, Eero, Passaro, Marcello, Andersen, Ole B., Dinardo, Salvatore, Fleury, Sara, Guillot, Amandine, Hendricks, Stefan, Kurekin, Andrey A., Müller, Felix L., Ricker, Robert, Skourup, Henriette, and Tsamados, Michel

Published
2019

41. TanDEM-X multiparametric data features in sea ice classification over the Baltic sea.

Author

Marbouti, Marjan, Antropov, Oleg, Praks, Jaan, Eriksson, Patrick B., Arabzadeh, Vahid, Rinne, Eero, and Leppäranta, Matti

Subjects
SYNTHETIC aperture radar, SEA ice, SUPPORT vector machines, RANDOM forest algorithms, CLASSIFICATION, SEAWATER
Abstract

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes. [ABSTRACT FROM AUTHOR]

Published
2021
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43. Retrieving Sea Level and Freeboard in the Arctic: A Review of Current Radar Altimetry Methodologies and Future Perspectives

Author

Quartly, Graham D., primary, Rinne, Eero, additional, Passaro, Marcello, additional, Andersen, Ole B., additional, Dinardo, Salvatore, additional, Fleury, Sara, additional, Guillot, Amandine, additional, Hendricks, Stefan, additional, Kurekin, Andrey A., additional, Müller, Felix L., additional, Ricker, Robert, additional, Skourup, Henriette, additional, and Tsamados, Michel, additional

Published
2019
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45. Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative

Author

Paul, Stephan, Hendricks, Stefan, Ricker, Robert, Kern, Stefan, Rinne, Eero, Paul, Stephan, Hendricks, Stefan, Ricker, Robert, Kern, Stefan, and Rinne, Eero

Abstract

In order to derive long-term changes in sea-ice volume, a multi-decadal sea-ice thickness record is required. CryoSat-2 has showcased the potential of radar altimetry for sea-ice mass-balance estimation over the recent years. However, precursor altimetry missions such as Environmental Satellite (Envisat) have not been exploited to the same extent so far. Combining both missions to acquire a decadal sea-ice volume data set requires a method to overcome the discrepancies due to different footprint sizes from either pulse-limited or beam-sharpened radar echoes. In this study, we implemented an inter-mission-consistent surface-type classification scheme for both hemispheres, based on the waveform pulse peakiness, leading-edge width, and surface backscatter. In order to achieve a consistent retracking procedure, we adapted the threshold first-maximum retracker algorithm, previously used only for CryoSat-2, to develop an adaptive retracker threshold that depends on waveform characteristics. With our method, we produce a global and consistent freeboard data set for CryoSat-2 and Envisat. This novel data set features a maximum monthly difference in the mission-overlap period of 2.2cm (2.7cm) for the Arctic (Antarctic) based on all gridded values with spatial resolution of 25 km × 25 km and 50 km × 50 km for the Arctic and Antarctic, respectively.

Published
2018

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